AWS ElastiCache Support
ElastiCache caching strategy and invalidation patterns.
Adding a cache is easy. Keeping it correct is not: stale reads, cache stampedes when a hot key expires, and keys that never get invalidated. We pick the right pattern per data type, and measure the hit ratio so you know the cache is earning its cost.
When You Need This
The situations where this work pays for itself.
Signs it is time
- Your database is slow or expensive under read load
- Users see stale data after updates
- A hot key expiring causes a spike on the database
- You do not know if the cache is helping
- You need to decide what to cache and for how long
What you get
- Caching plan listing what is cached, how, and for how long
- Reference implementation or code changes for cache-aside and invalidation
- Stampede protection for hot keys
- Hit ratio dashboards and alarms
How we do it
A clear, safe process you can follow along with.
1. Choose what to cache
We rank read paths by frequency, cost and tolerance for staleness. Good candidates are expensive, read-heavy and change rarely. We avoid caching data that must always be exact.
2. Pick the pattern
Cache-aside (read from cache, fall back to the database and populate) suits most reads. Write-through updates the cache with each write for fresher data. Write-behind trades safety for speed and needs care.
3. Define keys and TTLs
We use versioned, namespaced keys and set TTLs from business tolerance for staleness. We add random jitter so many keys do not expire at once.
4. Plan invalidation
We delete or update keys on writes in the same code path, use versioned keys to avoid deleting many keys, and for cross-service changes publish events to invalidate.
5. Prevent stampedes
For hot keys, we use request coalescing, short locks or early refresh so one request rebuilds the value while others wait or serve the previous one.
6. Handle cache failure
The application must still work if the cache is down or cold, with timeouts and circuit breaking so it does not hammer the database.
7. Measure
We calculate hit ratio from CacheHits and CacheMisses and track database load, so tuning is based on evidence.
Mistakes we help you avoid
Common ways this goes wrong, and what we do instead.
No TTL on anything
Stale data and memory growth follow.
Synchronized expiry
Many keys expiring together flood the database. Add jitter.
Caching errors and nulls without a plan
Negative results need short TTLs, or users stay stuck on a failure.
Frequently Asked Questions
What is the best caching strategy for a web app?
Cache-aside with sensible TTLs is the usual starting point. Add write-through or event-based invalidation for data that must stay fresh.
How do I avoid a cache stampede?
Add jitter to TTLs, coalesce concurrent rebuilds of the same key and refresh popular keys before they expire.
What is a good cache hit ratio?
It depends on the workload, so there is no universal target. Compare hit ratio with the database load it saves and tune from there.
Want Help With ElastiCache caching strategy and invalidation patterns?
Schedule an appointment. Tell us about your environment and we will outline the safest, highest-value approach.
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